A modification of Veto logic for a committee of threshold logic units and the use of 2-class classifiers for function estimation PublicDeposited

Descriptions

The well-known local adjustment algorithm for training a
threshold logic unit, TLU, is extended to a local adjustment
algorithm for training a network of TLUs Computer simulations
show that the extension is unsatisfactory.
A new logic for a committee of TLUs, called modified veto logic,
and a local adjustment algorithm for training a modified veto committee
are described. Unlike a majority committee, a modified veto
committee may have members added during training, and the modified
veto committee is free to attain a size needed to solve a problem.
Computer simulations show that a modified veto committee can solve
difficult pattern recognition problems and, in the instances tested,
does so more successfully than a majority committee. A technique for using a number of 2-class classifiers to perform
function estimation is described. The 2-class classifiers are trained
on a set of ordered pairs belonging to the function being estimated; no
information about the form of the function is needed; the function can
have any number of independent variables; and the accuracy of the
estimate increases with the number of 2-class classifiers used.
Computer simulations on artificially generated data and on "real life"
data show that this technique provides accurate estimates of functions.
It is shown that replacing non-binary variables by binary
variables can greatly increase the recognition rate of a TLU.